Quantitative Plant Biology

The root systems of wild tomatoes (S. Pimpinellifolium) can be understood as biological networks in which the lateral roots branch from a single main root and together balance two competing objectives: minimizing the material cost of building the network (wiring cost) and minimizing the transport time from the root tips to the shoot (conduction delay). Our prior work showed that S. Pimpinellifolium root architectures cluster near the Pareto-optimal front defined by these two objectives, with morphological diversity resolving into four qualitative topologies ((Chandrasekhar and Julkowska, 2022)). That framework assumed lateral roots grow as straight lines - ignoring gradual onset of lateral root gravitropism, the tendency of roots to curve toward the gravity vector. Because curved trajectories are longer than straight lines, gravitropism directly increases both wiring cost and conduction delay, constraining which architectures are physically realizable and thereby reshaping the Pareto front itself. Here we extend the model to explicitly incorporate lateral root gravitropism, producing predicted architectures that align much more closely with observed S. Pimpinellifolium root systems. We present a computational method to infer gravitropic sensitivity directly from anatomical tracing data - without reorientation assays - and apply it to 2423 arbors across different root topologies, growth conditions, and hormone treatments. Incorporating gravitropism reveals variation invisible to the straight-line model:notably, lateral roots show reduced gravitropic sensitivity under salt stress, mirroring a phenomenon previously described only for main roots and overlooked in lateral roots until now.

Mechanistic models of plant regulatory networks typically require extensive parameterization, limiting their generalisation and scalability. Here we present a parameter-free, topology-driven model of shoot branching that predicts phenotypic outcomes from network structure alone. We constructed a signed, directed causal network by distilling regulatory relationships from the published literature spanning many laboratories, species, years, data types, and methodological frameworks. This extracted the essential logic of the system, consistent with developmental-biological reasoning and anchored in empirical evidence. Using PSoup, which automatically translates network topology into algebraic equations, the model propagates information across the network and predicts the qualitative direction of change relative to a defined baseline, mirroring the comparative framework of biological experiments. The pipeline, from network construction through automated equation generation to prediction, is transparent and reproducible. Trained against branching phenotype data with 78 diverse perturbations spanning genetic mutations and hormone treatments, the model achieved 86% accuracy in predicting branching direction. On an independent test set of 84 perturbations measuring bud release and gene expression at nodes not used during training, accuracy reached 75%. The approach highlighted deficiencies in our understanding of the topology of the network around SMXL 6/7/8 and ABA nodes. Other errors came mainly from modelling choices, such as the threshold for scoring a node as changed relative to baseline. Beyond shoot branching, this work demonstrates a general strategy for synthesizing biological knowledge into validated predictive networks, providing a foundation for both applied breeding and the advancement of fundamental biology.

Leaf reflectance spectra are emerging as a viable substitute for gas-exchange measurements of photosynthetic capacity, with a community benchmark reporting that a spectrum accurately recovers most Farquhar-von Caemmerer-Berry (FvCB) parameters. This study re-scores the recovery under dataset-blocked, species-blocked, and leave-one-dataset-out designs, measuring the split-half reliability of each curated parameter. We constructed a convolutional encoder that maps a spectrum to the four parameters through a fixed, differentiable FvCB decoder trained on measured assimilation. A conspecific of 97.4% of held-out leaves were present in the training set, and accuracy is lost along the dataset axis but not along the species axis. Under blocked evaluation, a spectrum constrains a single capacity axis. Jmax25 retains only 17% of its recovery when Vcmax25 is held constant, and the Jmax25:Vcmax25 ratio is not predicted above a median null. The curated values of TPU25 are not reproducible, whereas those of Rday25 are well determined, but its recovery fails due to the loss. The published study measures interpolation rather than transfer, and spectra constrain less of the FvCB parameter space than assumed, including the carboxylation to electron transport balance. Routing predictions through explicit biochemistry makes identifiability measurable, although it does not improve prediction accuracy.

Auxins are a family of plant hormones involved in various processes across plant tissues and species. The Nuclear Auxin Pathway (NAP) consists of interacting transcription factors (ARFs) and repressors (Aux/IAAs), which govern an individual cells response to changes in auxin concentration. These components are present in all land plants, and many species possess multiple copies of each signalling component. We present a general framework for ODE-based models of NAP submodules with the flexibility to model the promotion and repression of target genes by any combination of transcriptional regulators. We analyse published data and show that auxin treatment in Arabidopsis thaliana roots triggers a range of characteristically distinct temporal response profiles--for both target genes and the signalling components themselves. Using our modelling framework, we recapitulate aspects of this behaviour by presenting examples of real and theoretical NAP subnetworks, and by analysing the effect that these network dynamics have on auxin-mediated transcriptional responses. This work demonstrates the utility of our modelling framework as a general-purpose tool for understanding the function of certain protein-protein and protein-DNA interactions through their effects on the NAP. This exploration of the rich dynamics of more complex signalling pathways promises to advance our understanding of the NAP.

Root hairs (RH) are excellent model systems for studying cell size and polarity since they elongate several hundred-fold their original size. Their tip growth is regulated by both intrinsic and environmental signals and is associated with the existence of a highly controlled cytoplasmic tip Ca{superscript 2} gradient, whose disruption impairs RH development. The molecular mechanisms underlying the Ca2+ homeostasis fine tuning and the Ca2+ organellar contributions to the cytoplasmic pool remain unclear. In the model plant Arabidopsis thaliana, many efflux routes are present, including those that employ Ca2+-pumps from the Autoinhibited Ca2+-ATPase (ACA) family. Here, we identified that the ER localized ACA2, and to a lower extent ACA7, are crucial ACAs required to control RH growth. By using genetically encoded Ca2+ biosensors we showed that Ca2+-dynamics are compromised in the aca2-2 mutant, having lower cytosolic Ca2+ concentration [Ca2+]cyt and growth rate, showing an altered homeostatic calcium setpoint compared to Col-0. Accordingly, the ACA2 mutation changed the dynamics of [Ca2+]cyt oscillations coupled to growth rate, inducing longer periods and more regular oscillations in the dominant high-frequency range (around 22 s), and slower oscillations (around 1 min) in the low-frequency range. Finally, expression of ACA2 with changes in four putative Ca2+ binding residues (ACA2{Delta}Ca2+) failed to rescue the RH growth phenotype in the aca2-2 mutant. Collectively, our findings indicate that ER-localized ACA2 and possibly ACA7 are crucial for modulating cytoplasmic Ca2+ signals, possibly composing a critical part of a negative feedback loop, and their absence leads to impairments in RH cell elongation.

The article introduces a new Forest Stress Index (ISF) based on a plant hydraulic modelling approach rather than classical climatic drought indices. Unlike other index like scPDSI or SPEI, ISF is grounded in xylem embolism dynamics simulated with the mechanistic SurEau model. The goal is to better link climatic anomalies to tree physiological functioning and mortality risk. ISF is defined using a locally adapted ideotype characterized by an optimal P50 value under a reference hydraulic functioning threshold. Simulations are performed across Europe and France using multiple climate datasets. The index is robust to model parameterization choices and assumptions about plant functional traits. Results show strong spatial and temporal consistency and significant correlations with SPEI and scPDSI. However, ISF more strongly highlights extreme drought years and exhibits a more skewed distribution. Future projections under SSP5-8.5 indicate a widespread increase in hydraulic stress with strong regional contrasts. Overall, ISF provides a mechanistic and complementary drought indicator more directly linked to forest mortality processes.

Photosynthesis is among the most consequential yet genetically complex traits in crop plants, and translating its natural variation into actionable genomic targets remains a central challenge for breeding climate-resilient varieties. To start addressing this, researchers are generating increasingly large, multi-environment field photosynthesis datasets. Yet, these data have been structurally under-analysed since their inception. Here we report the outcomes of the first dedicated hackathon focused on computational mining of such field data held in Accra, Ghana, in March 2026. Bringing together data scientists, plant physiologists, geneticists, and breeders from Europe and Africa, these interdisciplinary teams used photosynthetic data collected with hand-held fluorometers to genome-wide marker data across four crop species: cowpea (Vigna unguiculata), barley (Hordeum vulgare), common bean (Phaseolus vulgaris), and potato (Solanum tuberosum). Despite using different species and methods, independent teams identified the same three key findings. First, mechanism-informed feature engineering and dynamic modelling recover genetic signals that are not detected or discarded in standard analysis pipelines, resulting in traits with improved heritability and meaningful associations with yield. Secondly, machine learning methods proved effective at uncovering genetic associations, with temporally resolved features substantially outperforming single time-point measurements. Third, raw chlorophyll fluorescence and absorbance traces consistently contained more information and predictive power than the extracted parameters currently used. A defining feature of this event was having experimentalists and data scientists working together, enabling AI approaches to be grounded in domain knowledge and biological mechanisms rather than relying on data alone.

1O_LIEmbolism, the formation of air bubbles in the plant water transport system, is a mechanistic driver of plant death. The Optical Vulnerability Technique (OVT) is an imaging method for non-invasive quantification of embolism (including P50, a common metric for drought vulnerability), which can also provide detailed spatial and temporal information. Its major cost lies in the post-processing of thousands of images. C_LIO_LIHere we designed, tested, trained, and make publicly available a neural network model to automate post-processing of OVT images. Using a dataset of 65 leaves from Senecio pterophorous, we compared our model predictions to results obtained via traditional post-processing by an expert. C_LIO_LIOur model resolved P50 to within 0.027 MPa of the expert-processed data with training taking 30 minutes to 2.5 hours and model-runtime in the order of seconds to minutes, demonstrating its promise for increasing the efficiency and throughput of P50 calculation. The models performance in replicating the pixels that constitute embolism events was lower (mean event-frame IoU of 0.38). C_LIO_LIWe invite the community to utilise our model but emphasise that it does not replace the expert-processing pipeline and that care must be taken when considering applying this and similar approaches to OVT data. C_LI

Multi-environment trials are central to cultivar evaluation because they reveal how candidate cultivars perform across locations, years, management conditions, and stress environments. The resulting yield matrix is a rich source of data on genotype-by-environment interaction, and a wide literature on estimation, decomposition, visualisation, and prediction of yield potential and stability has flourished. However the ultimate question of which cultivar to recommend on the basis of such a matrix is often left implicit. The question is far from trivial, and in this paper we formulate cultivar recommendation as an axiomatic ranking problem. This framework is rich enough to encompass the existing literature on stability indices, as well as any other deterministic ranking procedure. We show that many commonly used stability-based procedures can violate minimal criteria of efficiency or consistency. The result of such violations is that a cultivar with uniformly high yield could be ranked below a cultivar with uniformly low yield, or the relative ranks of two cultivars could depend on whether or not a third cultivar is present in the matrix. Our results prove that under a small number of such criteria the space of admissible rules collapses to the family of power means and their limiting cases. If we further wish to allow multiplication normalisation of yield, we are left with the geometric mean as the unique solution.

All living organisms rely on the movement of ions across cell membranes as the fundamental physical basis of their internal energy and signaling, and plants are no exception. Plants perceive, integrate, and respond to environmental stimuli through electrical signals, classified as action, variation, and system potentials, that are coupled with calcium waves, reactive oxygen species, and hydraulic and hormonal changes to coordinate whole-organism responses despite the absence of a nervous system. Yet most studies characterize these signals using a single feature, such as amplitude or spike duration, in a single tissue, an approach that cannot establish how such signals correspond to the underlying ionic activity, mobility, and structural complexity of the signaling environment, or how this correspondence varies across organs. Here, we correlate plant bioelectrical signals with potential ionic energy flow using a multi-domain framework, combining discrete spike events, continuous waveform properties, spectral composition, and signal complexity applied to leaf, stem, and root recordings from tomato (Solanum lycopersicum) exposed to different stimulus. Electrical activity with increased stimulus strength, likely reflecting increased ionic flow, with the root showing the largest response. This suggests plant electrical signaling works as a distributed, ion-based information system, useful for stress monitoring and bio-inspired sensor design.

Climate change represents a challenge to food security by interfering with the environmental conditions needed for productive plant growth. While technology can be used for partial mitigation, access to technology is inequitable. Low-cost microcontrollers, such as the ESP32, have recently lowered the barrier for entry into prototyping smart devices. ESP32s equipped with capacitive moisture sensors have been suggested for low-cost smart plant watering systems. However, measuring moisture in soil is complex, potentially destructive, and requires careful calibration in order to characterise the response curve mapping soil water content to sensor measurements. Here, we developed a Bayesian method for estimating the inverse response curve from capacitive moisture sensor data, known water doses, and prior uncertainty, bypassing the need for destructive gravimetry. This method constitutes the core calibration module of the open-source OpenHCult software system for low-cost horticultural automation.

Mechanical stimulation of the root triggers signal transduction involving Reactive Oxygen Species (ROS) and calcium, but their relationships are unclear. This study aims to clarify the temporal and spatial interrelations between calcium and ROS following a localized lateral compression of the root. We combined a microfluidic valve rootchip to apply controlled compression, with fluorescent probes and wide-field or confocal microscopy to monitor H2O2 and calcium dynamics in root tissues simultaneously. Pharmacological inhibitors were used to investigate the causal links between H2O2 and calcium responses. In response to compression, we observed transient H2O2 accumulation, with characteristics similar to the calcium response observed previously in the same microfluidic system. H2O2 and calcium response occurred in 3 kinetic phases: a fast calcium increase relying on mechanosensitive channels and external calcium entry, followed by a long-lasting H2O2 accumulation and a slow calcium increase depending on NADPH oxidase activity. H2O2 accumulated in all root tissues while calcium increases were confined to the root center. These results suggest that two mechanotransduction mechanisms are involved in root response to compression. One mechanism relies on plasma membrane mechanosensitive channels, triggering a fast calcium increase. Another independent mechanism, relying on FERONIA, induces H2O2 accumulation, which drives the slower secondary calcium increase.

Early, precise, and non-destructive stress detection is essential for maintaining crop productivity, particularly in high-density plant growth systems like controlled environment agriculture (CEA), where manual monitoring is often impractical. Using plant motion as a proxy for growth and plant health, we demonstrate a method for early, non-invasive stress detection through quantitative leaf-movement analysis in lettuce and five other CEA relevant crops. Leaf-movement dynamics under stress were imaged with a low-cost, scalable Raspberry Pi imaging setup and quantified using a repurposed open-source motion estimation algorithm; Tracking Rhythms in Plants (TRiP). Our system detected stress-induced changes in leaf-movement within 1 hour of stress, with the timing dependent on the nature of the stress. Sustained reductions in leaf-movement coincide with decreased biomass accumulation. This approach offers a non-invasive, rapid, scalable, and cost-effective solution for continuous crop monitoring, with potential for application in both terrestrial and space farming CEA systems. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=138 SRC="FIGDIR/small/732190v1_ufig1.gif" ALT="Figure 1"> View larger version (54K): org.highwire.dtl.DTLVardef@19ee20eorg.highwire.dtl.DTLVardef@b0804org.highwire.dtl.DTLVardef@3b3fa8org.highwire.dtl.DTLVardef@1d04026_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstract:C_FLOATNO Quantification of leaf-movement dynamics as a high-throughput proxy for plant physiological status, enabling early stress detection and timely intervention to mitigate yield penalties in CEA settings (image made with biorender.org). C_FIG

O_LIThe effect of some bioactive compounds on living organisms is dependent on their concentration and gradients, as is the case of hormones and signalling peptides, determining cell identity, activity and organism development. C_LIO_LIThere are a handful of methods that allow to produce spatially confined peaks of concentration local application of biochemicals on plants, such as agar blocks and microinjection, but they lack in precision, throughput and/or simplicity. C_LIO_LIWe developed the MicroTron, a microfluidics-based method specifically for filamentous organisms or life cycle stages, like the moss plant Physcomitrium patens protonemata, that serves as a platform for the application of chemicals on single cells and study the cell response. C_LIO_LIWe show how chemical applications could be performed on cells, either on the side or apically with dyes and hormones, targeting the cell wall, cell membrane, cytosol and nucleus. C_LIO_LITreatments could be applied on single filaments and with a precision of up to single cells in optimal conditions. C_LIO_LIThis method could be used to study live responses to chemicals with high spatiotemporal resolution. C_LI

Shoot branching is a primary determinant of plant form and crop yield, yet whether auxin or carbon supply is the proximal regulator of branching remains contested. In Arabidopsis, an earlier report that exogenous auxin fails to restore apical dominance after decapitation has been taken to weaken the case for auxin, and several studies have proposed that sugars are the primary regulator. Resolving this has been difficult because most perturbations of sugar status also disturb auxin. Here, we revisit the control of Arabidopsis branching using well-controlled, largely unperturbed plants and approaches designed to isolate each pathway. Contrary to the earlier report, apically applied auxin restored the suppression of rosette branching after decapitation, placing Arabidopsis in line with other species. In a dataset of 718 plants, cauline and rosette branching were weakly but significantly negatively correlated, consistent with a polar-auxin-transport-based model and contrary to a previous conclusion of no relationship. Removing all rosette leaves at bolting slowed bud growth but did not alter the final number of branches. Varying photosynthetic photon flux density across six natural accessions, analyzed by piecewise structural equation modeling, showed that photoassimilate acted far more strongly on the mass deposited into branches than on whether a bud initiates a branch. We conclude that auxin remains a major regulator of apical dominance in Arabidopsis, and that photosynthetic assimilate, while required as a substrate for branch growth, contributes little to determining branch number but more to branch size and biomass.

Accurate segmentation of plant disease symptoms is essential for crop monitoring and phenotyping, yet it typically requires costly pixel-level annotations. Weakly supervised semantic segmentation (WSSS) alleviates this burden using image-level labels, but its performance depends on the quality of spatial priors such as class activation maps (CAMs). We investigate whether text-guided segmentation with the Segment Anything Model 3 (SAM3) can serve as an alternative weak supervision signal. Three pseudo-mask generation strategies are compared: (i) CAMs refined with SAM or SAM3, (ii) zero-shot text-guided SAM3, and (iii) a hybrid approach combining weak spatial cues with text prompts. The resulting pseudo-masks are used to train a DeepLabV3 model. Text guidance alone matches or outperforms conventional WSSS, achieving up to 0.46 IoU without spatial supervision and 0.61 IoU on a public dataset, although performance is sensitive to text prompt formulation. The hybrid strategy improves robustness, reaching 0.50 IoU on the primary dataset and 0.58 IoU on the additional dataset while reducing prompt sensitivity. Overall, text guidance is a promising alternative to conventional weak supervision, while hybrid approaches provide a more robust solution for plant disease segmentation.

The sequence of leaf physiological thresholds underlying plant responses to water deficit is thought to be functionally coordinated; yet, to what extent this coordination is maintained across genotypes and environments remains poorly documented at the intraspecific level. We characterized the sequence of stomatal closure, turgor loss and xylem embolism in the leaves of two genotypes of the riparian species Populus nigra (DRA-038 vs. PG-31) subjected to control, additional nitrogen or additional potassium treatments. Under control conditions, embolism measurements using the optical vulnerability method showed that DRA-038 was more vulnerable than PG-31, in agreement with measurements performed on stems with the reference Cavitron method. Stomatal closure consistently preceded xylem embolism, while bulk leaf turgor loss was typically observed once xylem embolism had already reached 50%. Hydraulic thresholds responded to treatments in a genotype-dependent manner, the intrinsically more vulnerable genotype DRA-038 being typically more plastic. However, despite variations across genotypes and treatments, the trait sequence remained tightly coordinated such that stomatal safety margins (SSMs) remained virtually null. These findings support a strong mechanistic integration of leaf hydraulic thresholds in poplar across genetic units and varying environments, questioning whether to favour intrinsic tolerance or plastic capacities in breeding future drought-tolerant genotypes.

Plants acclimate to low temperatures by remodeling carbohydrate metabolism. Permanent cold at 4/5{whitebullet} C induces well-characterized cold acclimation in plants, including accumulation of starch and soluble sugars. In natural environments, however, annual plants in their vegetative phase more frequently experience chilling nights (0-6{whitebullet} C nights followed by days at least 12{whitebullet} C warmer), whose metabolic consequences remain poorly understood. We investigated Col-0 Arabidopsis thaliana exposed to one or seven chilling nights and quantified central carbohydrate metabolites, starch, photosynthetic parameters, and maximal activities of enzymes linking sucrose synthesis and cleavage. We integrated these time-series data into a biologically constrained augmented neural ordinary differential equation (ANODE) model to infer diurnal reaction-rate dynamics. Chilling nights induced strong accumulation of sucrose, glucose, and fructose, particularly after seven nights. Both measured sucrose phosphate synthase (SPS) capacity and ANODE-predicted SPS rates increased. In contrast to permanent cold acclimation, daytime starch over-accumulation was absent. Thus, lacking starch over-accumulation together with increased SPS rates, chilling nights invoke distinct carbohydrate-partitioning patterns towards sucrose, indicating a fundamentally different acclimation strategy compared to well described permanent cold.

O_LIThe chloroplast CLP chaperone-protease is essential for chloroplast biogenesis. CLP substrate selection is aided by the N-recognin CLPS1 and CLPF adaptors. They interact with each other and the CLPC1 chaperone, but their specific functions are poorly understood. C_LIO_LIWe employed in vivo CLPC1 substrate-trapping in Arabidopsis by expressing a 35S:CLPC1-TRAP-STREPII transgene in wild-type (WT), clpf, clps1, and clpfclps1 to test the consequences of the loss of these adaptors on CLPC1-trapped proteins. Immunoblotting and protein half-life experiments were carried out for identified CLP substrates. C_LIO_LIExpression of the 35S:CLPC1-TRAP-STREPII in clps1cpf was embryo lethal. CLPF was trapped at a reduced level in clps1, supporting CLPS-CLPF interactions. Chloroplast 1O2 sensor EXECUTER1 (EX1) was trapped in WT and clps1 but not significantly in clpf. Steady-state protein accumulation of EX1 and its homolog EX2 increased 30-fold in the CLPC1-TRAP lines and clpr2-1, but not in clpf or clps1. In planta experiments showed that the half-life of EX1 is [~]3-fold longer in clpc1-1 than in WT, but EX1 half-life was unaffected in clpf. C_LIO_LIWe conclude that the CLP system plays a key role in EX1,2 homeostasis by keeping their intra-chloroplast concentrations low through continuous degradation, upstream of their 1O2 signaling function. C_LI

Far-red light (FR; 700-750 nm) is increasingly incorporated into controlled-environment lighting because it can improve photosynthetic efficiency when combined with comparatively shorter wavelengths. In long-day leafy crops such as spinach, however, FR may also promote the transition from vegetative to reproductive growth and thereby reduce marketable yield. Most studies have evaluated FR fraction, intensity or end-of-day exposure, whereas the developmental timing of FR has rarely been tested, particularly in spinach. Here, we evaluated six commercial spinach cultivars (Amador, Harp, Renegade, Responder, Rubino and Santa Cruz) in an indoor vertical farm under a common red-green-blue background (PPFD 260-264 {micro}mol m-{superscript 2} s-{superscript 1}, 12 h photoperiod, 24 {degrees}C) and four FR timing treatments: no FR (Control), FR throughout production (FullFR), FR during early development only (EarlyFR), and FR during late development only (LateFR). LateFR increased marketable fresh weight relative to Control (244 vs 224 g) and reduced flowering incidence, whereas far-red supplied during early development reduced fresh weight (158 g) and increased flowering. The magnitude of the timing response differed among cultivars: switching from EarlyFR to LateFR recovered 0 % fresh weight in Amador but 107 % in Renegade and Rubino, with the largest penalties occurring in otherwise bolt-resistant cultivars. EarlyFR also increased total chlorophyll and reduced the chlorophyll a:b ratio. These results show that FR response in spinach is strongly conditioned by developmental stage and cultivar. Although LateFR received more total far-red than EarlyFR, it behaved like the Control, indicating that the penalty was set by far-red timing rather than dose. Treatment differences in bolting and yield tracked an estimated phytochrome photostationary-state deficit during early development: a phytochrome-deficit model markedly outperformed a cumulative-dose model ({Delta}AIC = 441), and the deficit x cultivar interaction was strong (p < 0.001), with bolt-resistant cultivars losing most yield when far-red coincided with the early developmental window. We therefore propose that FR should be treated as a genotype-dependent management variable rather than as a fixed spectral input, with late application and bolt-resistant cultivars offering the most favourable combination for vertical-farm spinach production. Framed within the breeders equation, the close match between the trial and production environment and the scope for shorter breeding cycles indoors suggest that genotype and far-red timing can be optimised jointly to accelerate genetic gain.